1. Ian Koenig Chief Architect – Thomson Financial
April 6, 2019
C4. Case Study: Event Processing as a Core Capability of Your Content Distribution Fabric
20 September 2007
Ian Koenig
Chief Architect – Thomson Financial

2. A framework for discussing Complex Event Processing
Agenda
A framework for discussing Complex Event Processing
“Elementized” News as a data source
The “fabric” for distributing content and its emerging capabilities for stream processing
Enabling new types of data sources creating new ‘opportunities’
Hinting at a larger pattern for distributing content and the role that complex event processing will play.

3. The CEP Framework model
April 6, 2019
The CEP Framework model
Content Sources
Content Streams
Stream Agents
User Interface
CEP Engine
Event Proc
Event Proc
News
Level 2
Level1
Event Proc
Application Logic
Stream Adapters
Other
New Content Streams
Stream adapters describe the incoming data streams so that they can be processed by the CEP engine
Stream agents read the streams and inject new data
SQL (with extensions) is the de-facto language used
The primary types of analysis are on a single stream “in time” or crossing two or more “streams”
“Don’t cross the streams” – Ghostbusters
Application logic is another type of “agent” that either generates a new stream to go outside the box or a user interface
And of course new stream can be new CEP inputs or in the case of algorithmic trading, a “FIX” order stream

4. Thinking Outside the CEP Box
April 6, 2019
Thinking Outside the CEP Box
Outside The CEP Box
CEP Engine
Application Logic
Stream Adapters
User Interface
New Content Streams
Event
Proc
Stream Agents
News
Level 2
Level1
Content Sources
Content Streams
Other
Most of the rest of this conference focuses on the CEP box
This discussion focuses “out of the Box”

5. Thinking Outside the Box
April 6, 2019
Thinking Outside the Box
The Classic 9 Dots Puzzle: Connect all 9 dots with 4 straight lines without ever taking the pencil off of the paper
As an aside, The genesis of “Thinking outside the box” is….
To solve the problem, you have to “think outside the box”

6. Content Sources and Content Distribution
April 6, 2019
Content Sources and Content Distribution
Content Distribution Fabric
Content Sources
Content Streams
Application Logic
Level 2
Level1
CEP Engine
Application Logic
Stream Adapters
Event
Stream Agents
News
Other
Lots of focus exists on market data sources as inputs to CEP
By market data, we mean Level 1 and Level 2 data. Pricing, Bids/Asks, Orders, Depth
The next content source of interest, and there is quite a bit of press about this, is News (no pun intended)

7. Elementized News Moves Markets …
April 6, 2019
Why News?
Elementized News Moves Markets …
….News Moves Markets ….
Aboutness
SEC will Allow Companies to use the Internet to Improve Investor-Management Communications          NA
From CFO.com - August 16, 2007
According to SEC chairman Christopher Cox, the commission will allow companies to use the Internet to improve investor-management communications. As currently proposed by the commission, a company interested in offering this venue to shareholders would alert them via
Genre
Facts
News is important to algorithmic trading because news “moves markets”
To make news “process-able” by computers we need to know four main things:
Aboutness: What the news is about within a well- defined concept system (or ontology). Aboutness is defined by: Entities and Subjects. These are the: Companies, People, Markets, Places, Events, the Economy, Human Interest, etc. that the news is “about”. In database ‘lingo’
, Entities and subjects are all Entities, but we differentiate those Entities that link other content sets (in our universe) together (like Companies, & people) and those that don’t. Those that do are Entities. Those that do not are “subjects”.
Genre: (or Type): Is this a story, feature, market report, blog, opinion, rumor, etc?
Sentiment: Is the Story generally positive, negative or neutral about the subject(s).
Facts: Specific numbers, such as Economic releases or Company Earnings that are tagged with XML elements
Aboutness, Genre, Sentiment and Fact “tags” are added both at the document level and marked up in-line. This is called Elementized News. Computers can aid the traditionally human process. This is called auto-categorization.
Sentiment

8. April 6, 2019
The Metaverse
The Metadata Universe (or Metaverse) is the set of Categories (Entities and Subjects) that provide semantic understanding for text and data.
Geography
Regions, Countries, Physical Features
Operates within
Industry
Sector Hierarchy (Multiple Schemes)
Market (Equity, Commod FI, et al)
Is grouped by
Analyst For
Organization Gov’t, Agency, Company , NGO Market Participant
Subsidiary of
Person
(Multiple Roles)
Analyst For
Officer of
Mkt. Part. – Provides Quotes
Listed (Market Participant)
Index For
Issues
Indicator For
Entity Model + Categorization Hierarchy
Instrument Security, Future, Derivative, et al
Index
Financial Indexes
Indicator
Economics, Market Stats
Event Corp. Action Meeting, et al
Has Quotes
Quote, Trade, IOI, Advertisement, Order

9. Categorization Mark-up Example
April 6, 2019
Categorization Mark-up Example
Entity: Pharmaceuticals - An Industry Entity
Entity: Schering-Plough (SGP-US) – An Organization of type: Company
Entity: Merck KGAA (MRK-US) - An Organization Entity of type: Company
Auto-categorization and Entity Extraction Use case
The company Merk (which is a type of Organization) is referenced. Once identified, we know the ticker symbol and therefor can bring in prices
The same is true for Schering Plough.
Both are in the Pharmaceuticals industry which we can deduce both by the text of the story and by the associated industry of the referenced companies
Finally, you may notice that we do not identify drugs, which if we were “Thomson Scientific”, we would. These are not “yet” part of the entity model for Thomson Financial

10. NewsML Mark-up example
April 6, 2019
NewsML Mark-up example
Document Level Mark-up (Categories only)
...
<subject type="type:subject" qcode="CategoryId: " creator="org:thomson"/>
<subject type="type:subject" qcode="CategoryId: " creator="sys:care" why="why:machine-generated" confidence="70" relevance="65"/>
In-line Markup (Categories + Facts)
<body>
...
<p>The <toc:Category xsi:type="toc:Indicator" IndicatorId=" ">unemployment rate</toc:Category> fell one-tenth of a percentage point to 5.4 percent, the lowest rate since October 2001, primarily because 152,000 adults dropped out of the labor force.</p>
<p>"We were encouraged to see the headline payroll number meet expectations after two months of disappointments," said <toc:Category xsi:type="toc:Organization" OrganizationId=" ">SunTrust Robinson Humphrey</toc:Category> analyst <toc:Category xsi:type="toc:Person" PersonId="122456">Tobey Sommer</toc:Category>. The report, he said, "is likely to improve investor sentiment on employment-related stocks."</p>
<p> <toc:Category xsi:type="toc:Quote" QuoteId=" ">Manpower (MAN-US)</toc:Category> shares led the gainers, rising 2.5 percent to $ <
</body>
Sample NewsML

11. Auto-categorization Technology
April 6, 2019
Auto-categorization Technology
Much Financial, Legal and Medical information exists in the form of textual documents
Traditional “Editorial” processes to tag/index documents can no be augmented by algorithms that can achieve very high precision (~95%) against very large ontologies (10,000’s of terms)
Thomson employs a technology called CaRE (Categorization and Recommendations Engine) to do this, which originated in the Thomson Legal and Regulatory division.
CaRE uses a set of statistics-based algorithms that are trained to understand a specific ontology as a concept scheme.
Autocat technology

12. Elementized News – Summary
April 6, 2019
Elementized News – Summary
Each News story is tagged at three levels.
Document Level: The overall story lists all the category metadata (Entities + Subjects + Genre + Sentiment) for the story.
In-line Entities: Each initial reference to an Entity is marked up “in- line” in the document for additional context.
In-line Facts: Specific Numeric Elements (e.g. US GDP or Thomson Q3 Revenue) are tagged using XML elements
In-line News vs. Document level Mark-up
Sentiment tags (e.g. positive earnings or negative rating) and Subject tags provide semantic understanding of the news story
Numeric Facts (when Elementized) are directly process-able by algorithms.
Entity tags (e.g. Company references) allow news to be linked and correlated to Market data streams by CEP engines, for example, to make trading decisions
The Value of News Elements
Entities are the well defined business objects that link financial content sets together.
Subjects (or topics) include: Entity Groupings, Entity types, and other classifiers of interest that are not distinct “entity nouns”
Subjects + Entities = Categories

13. Content Sources and Content Distribution
April 6, 2019
Content Sources and Content Distribution
Content Distribution Fabric
Content Sources
Content Streams
Application Logic
Level 2
Level1
CEP Engine
Application Logic
Stream Adapters
Event
Stream Agents
News
Other
Lead in to the content distribution fabric
So we’ve talked a bit about market data streams and now about news data streams. We know about the complex event processing logic that consumes these streams.
So lets talk about the distribution fabric that lies between the content sources and the CEP applications.

14. The Content Distribution Fabric
April 6, 2019
The Content Distribution Fabric
Intermediation
Service Provider
Service Consumer
Service Contract
Service Broker
Find
Bind
Register
Initialization
Service Provider
Service Consumer
Synchronization
Service Provider
Service Consumer
Content Aware Network
The Fabric itself provides three main functions:
Intermediation – The process by which we connect service providers (content sources) and service consumers (CEP applications). These must be “loosely coupled” so we provide a meta-data driven service broker to connect the two. By loose coupling we mean simply: the ability to make a breaking change in the interface (i.e. non-backwards compatible change) without breaking the application and we can move from the previous version to the next version by upgrading provider and consumer independendently, one at a time and in either order. The Service broker provides: “Register, Find and Bind” (explain)
Initialization – is the process by which we get an empty application up to a known “synchronization checkpoint” as defined
Synchronization

15. XSLT XPATH X XML x “X” Marks the spot April 6, 2019
XML as the Enabling technology
XPATH
The XML Path Language (XPath) is an expression language that allows for addressing parts on an XML document. XPath models an XML document as a tree in which elements, attributes, and contents are nodes. Its name is
derived from the use of the slash separator to descend the branches of the tree. An XPath expression “selects” a node-set (one or more nodes), a string, a number, or a Boolean.
XSLT
XSLT is typically used to transform an XML document into another XML document. It does this with style sheets. XSLT stylesheets are similar to Cascading Style Sheets, with two
exceptions: XSLT stylesheets are written in XML, and XPath is used to determine which parts of the original document to transform.
But XML has always been “too big” and “too slow” except for niche use. Or has it?

16. Content-Aware Hardware Infrastructure
Mobile
Devices
Applications
Databases
Content-Aware
Network
IP/MPLS
Routing Module
Transformation Module
Advanced Interface Module
Assured Delivery Module
500, 000 routes
1000’s xforms / sec
>1MM msgs / sec
Active/active fail-over
We use hardware from Solace systems to provide the underlying content aware network that lays the foundation for the synchronization capability. We call this the “Message Bus”
Ultra low latency
JMS + HTTP + “native” interfaces
Use of XML as the message format which is now “enabled” due to advances in hardware based acceleration, compression, routing
XPATH as subscription language
XSLT to make messages smaller (fewer fields)
Ultra low latency across the bus (0.7 ms for a 4K XML message)
Use of JMS + HTTP + native interfaces to access
0.7ms transit for a 4K XML document
CONFIDENTIAL

17. New Streaming Content Sources
April 6, 2019
New Streaming Content Sources
Content Sources
Level1
Content Distribution Fabric
(Intermediation, Initialization, Synchronization)
CEP Engine
Application Logic
Stream Adapters
Event
Stream Agents
Complex Event Processing Applications
Content Streams
Level 2
News
Research
Briefings
Filings
Deals (M&A)
Financials
Now that we have the basic “event streaming capability” we can use that to create data streams for content sets that we do not traditionally think of as “streaming” such as:
Research
Estimates
SEC Filings
Company Financials
M&A
Lots more
Because in essence we can learn lessons from market data that are transferrable to all other content sets. And by moving to an event-oriented mindset we can not only increase business value, but we can also reduce network traffic and system load in the end because even though XML is “big” and “slow”, we have techniques to manage that and only sending the data that changes has a clear “mathematical” advantage on big, slow moving data sets. We had one case where a 5G database was “replicating” 100GB / day. Moving to an eventing model will reset that to 10MB /day, because that’s all the data that actually changes. That’s a 1000x fold decrease in overall aggregate network traffic.
Estimates …

18. The Entity Model vs the Relational Model
April 6, 2019
The Entity Model vs the Relational Model
Relational Data
Physical
Table
Logical
Entity
Business Entities
Canonical Business Entities
XML
Transform
The relational model vs the Entity Model
Relational models are great for storing data. They are physical models. They are not how we “use” the data.
When people use RDBs they often have to restructure the data into logical business entities or objects to make the data more usable to the application logic.
We use the Entity model (encoded in XML) as the canonical data model for transporting data across the Enterprise. This model is very powerful. First, it forces the unit of data transfer to be logical elements that mean something to the business (not tables encoded as files with fragile foreign keys). The data is structured hierarchically which is very different than the way it is structured in the RDB and much more object oriented. What comes out are objects that “feel” more like financial business objects and events and feel more like financial business events.

19. Changed Data Capture as an Enabling technology
April 6, 2019
Changed Data Capture as an Enabling technology .
Content Source 1
Changed Data Capture (publish)
Transform 4
Content Distribution Fabric
XML 2
Table
Table
Triggers or
Log Mining
Table
Transaction
Log 3
Changed Data Capture technology as an enabler of Event Streams
But to get these traditional databases to stream, you need new “enabling technology”
Enter “Changed Data Capture”
Changed Data Capture technology watches the transaction logs of traditional databases and can create messages when transactions are committed.
Furthermore, they have transformation capability, so that those messages need not follow the relational data model of the database. Instead, in our case they follow an Entity or Object model
Some CDC tools are better than others at transformation. Some will work in conjunction with a more traditional ETL tool where sophisticated transformations are needed.
This ETL process is what we use to create the initialization file.
2: Database Triggers – Database triggers can be used to generate events, but this is not recommended
1: Publishing pipeline – For Databases built using a “publishing pipeline pattern”, events can be generated directly
3: Log Mining – is a technique that watches the transaction log that modern databases use to capture all changes as they are made.
4: Transformation– The final step is transforming the transactional changes made to the databases to XML messages that capture the “business event” process-able downstream.

20. Content Distribution Pattern
April 6, 2019
Content Distribution Pattern
Content Distribution Fabric
(Intermediation, Initialization, Synchronization)
Canonical Data Model
(in XML)
The Application Database
Data Interface (Content Distribution)
Service Interface
Human Interface
Ingest Interface (Feeds + Authoring)
Content Source(s)
Metadata
The Enterprise Database
The Content Master
Lots of the literature refers to the Enterprise Database as this amorphous “mass” of uncoordinated data with SOA as the white night to slay the dragon of chaos. Maybe it is. But the real answer is that with any technique like SOA or OO or MDM or “pick you favorite acronym” comes a degree of architectural rigor that if properly applied both technologically and organizationally, allows the dragon of chaos to be slain.
This model learns the lessons from market data and datafeeds and applies those lessons widely across the world of content. What we learn is that The Enterprise Database is not a single thing. In fact we see two distinct classes of databases:
Content masters – who exist to house the single version of the truth
Application databases – who exist to make predictable queries, fast
Content masters – highly normalized, highly optimized for update, never delete data.
Application databases – Highly denormalized, optimized for retrieval.
Between them – Data Interfaces: Initialization files and Content Streams
And the Content Distribution Fabric provides the concrete implementation for:
intermediating publishers & subscribers
Initializing
Staying synchronized with changes

21. And if you Squint just a little tiny bit …
April 6, 2019
And if you Squint just a little tiny bit …
Content Master Database
Content Source(s)
Ingest Interface (Ripping)
Data Interface (Content Distribution)
Application Database
Service Interface
Human Interface
Metadata
And if you squint just a little tiny bit, you might have seen this pattern once before.
TF Information Architecture v0.7

22. The World of Event-Oriented Content
April 6, 2019
The World of Event-Oriented Content
Content Streams
Financials
Deals (M&A)
Level 2
Level1
News
Content Sources
Orders
IOIs
Research
Briefings
Filings
Estimates
And More
Content Distribution Fabric
(Intermediation, Initialization, Synchronization)
CEP Engine
Application Logic
Stream Adapters
Event
Stream Agents
Complex Event Processing Applications
The new world – The only constancy is “change”. All data “moves” Its only a matter of “how fast”. Key technologies are stream oriented databases, content distribution fabrics and a general awareness of the rules around Event Stream processing
In in this new world, all content has the potential to change “transactionally”. We have lots of interesting new content streams for CEP aware applications and a Content distribution fabric that itself has event stream processing capabilities.

23. The End
Q & A

24. Appendix
Appendix

25. Thomson Master Categories: Sample Structure
Canonical Terms are mapped to presentation terms at the most precise Level of the hierarchy. The presentation hierarchy contributes to search relevance.
Canonical
Presentation
Consumer Surveys
Business Surveys
Geography
3353 Categories
Industry
2482 Categories
Market
354 Categories
Surveys & Cyclical Indexes
Cyclical & Activity Indexes
Activity Index
Leading Indicator
Economics & Trade
GDP by Expenditure
Instrument Security, Future, Derivative, et al
Indicator
Economics, Market Stats
GDP by Industry
Incomes
National Accounts
Exports
Event
Corp Action, et al
Imports
Investment Capital
Money & Finance
Money Supply

26. Intelligent Network Hardware Performance
Messaging Throughput
(msgs/sec)
Tens of
thousands
>Million
Messaging Latency
(at 50% of peak load)
Milliseconds
Microseconds
Transformations
(sustained throughput)
MB/sec
GB/sec
Content Routing
(number of rules)
Small number
of thousands
Hundreds of
Thousands
Content Routing Latency
(with content rules)
Seconds
Microseconds
Persistent Messaging
(msgs/second)
A Few
Thousand
Many Tens of
Thousands
Software Infrastructure
Hardware Infrastructure